{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducing the Keras Functional API\n",
    "\n",
    "## Learning objectives\n",
    "  1. Understand embeddings and how to create them with the preprocessing layer.\n",
    "  2. Understand Deep and Wide models and when to use them.\n",
    "  3. Understand the Keras functional API and how to build a deep and wide model with it.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In the last notebook, you learned about the Keras Sequential API. The [Keras Functional API](https://www.tensorflow.org/guide/keras#functional_api) provides an alternate way of building models which is more flexible. With the Functional API, we can build models with more complex topologies, multiple input or output layers, shared layers or non-sequential data flows (e.g. residual layers).\n",
    "\n",
    "In this notebook you will use what you learned about preprocessing layers to build a Wide & Deep model. Recall, that the idea behind Wide & Deep models is to join the two methods of learning through memorization and generalization by making a wide linear model and a deep learning model to accommodate both. You can have a look at the original research paper here: [Wide & Deep Learning for Recommender Systems](https://arxiv.org/abs/1606.07792).\n",
    "\n",
    "<img src='assets/wide_deep.png' width='80%'>\n",
    "<sup>(image: https://ai.googleblog.com/2016/06/wide-deep-learning-better-together-with.html)</sup>\n",
    "\n",
    "The Wide part of the model is associated with the memory element. In this case, we train a linear model with a wide set of crossed features and learn the correlation of this related data with the assigned label. The Deep part of the model is associated with the generalization element where we use embedding vectors for features. The best embeddings are then learned through the training process. While both of these methods can work well alone, Wide & Deep models excel by combining these techniques together. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!sudo apt-get install graphviz -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow==2.6.0\n",
      "  Downloading tensorflow-2.6.0-cp37-cp37m-manylinux2010_x86_64.whl (458.3 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m458.3/458.3 MB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: clang~=5.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.6.0) (5.0)\n",
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      "  Downloading typing_extensions-3.7.4.3-py3-none-any.whl (22 kB)\n",
      "Requirement already satisfied: six~=1.15.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.6.0) (1.15.0)\n",
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      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard~=2.6->tensorflow==2.6.0) (3.10.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow==2.6.0) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow==2.6.0) (3.2.2)\n",
      "Installing collected packages: typing-extensions, tensorflow\n",
      "  Attempting uninstall: typing-extensions\n",
      "    Found existing installation: typing-extensions 3.10.0.2\n",
      "    Uninstalling typing-extensions-3.10.0.2:\n",
      "      Successfully uninstalled typing-extensions-3.10.0.2\n",
      "  Attempting uninstall: tensorflow\n",
      "    Found existing installation: tensorflow 2.6.5\n",
      "    Uninstalling tensorflow-2.6.5:\n",
      "      Successfully uninstalled tensorflow-2.6.5\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tensorflow-io 0.21.0 requires tensorflow-io-gcs-filesystem==0.21.0, which is not installed.\n",
      "tfx-bsl 1.10.1 requires google-api-python-client<2,>=1.7.11, but you have google-api-python-client 2.65.0 which is incompatible.\n",
      "tfx-bsl 1.10.1 requires pyarrow<7,>=6, but you have pyarrow 9.0.0 which is incompatible.\n",
      "tfx-bsl 1.10.1 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,<3,>=1.15.5, but you have tensorflow 2.6.0 which is incompatible.\n",
      "tensorflow-transform 1.10.1 requires pyarrow<7,>=6, but you have pyarrow 9.0.0 which is incompatible.\n",
      "tensorflow-transform 1.10.1 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,<2.10,>=1.15.5, but you have tensorflow 2.6.0 which is incompatible.\n",
      "tensorflow-serving-api 2.10.0 requires tensorflow<3,>=2.10.0, but you have tensorflow 2.6.0 which is incompatible.\n",
      "pydantic 1.10.2 requires typing-extensions>=4.1.0, but you have typing-extensions 3.7.4.3 which is incompatible.\n",
      "black 22.10.0 requires typing-extensions>=3.10.0.0; python_version < \"3.10\", but you have typing-extensions 3.7.4.3 which is incompatible.\n",
      "apache-beam 2.42.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.6 which is incompatible.\n",
      "apache-beam 2.42.0 requires pyarrow<8.0.0,>=0.15.1, but you have pyarrow 9.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed tensorflow-2.6.0 typing-extensions-3.7.4.3\n",
      "Collecting tensorflow-gpu\n",
      "  Downloading tensorflow_gpu-2.10.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (578.0 MB)\n",
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      "  Downloading absl_py-1.3.0-py3-none-any.whl (124 kB)\n",
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      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.7/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow-gpu) (0.2.7)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.7/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow-gpu) (4.9)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow-gpu) (4.2.4)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow-gpu) (1.3.1)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /opt/conda/lib/python3.7/site-packages (from markdown>=2.6.8->tensorboard<2.11,>=2.10->tensorflow-gpu) (4.11.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow-gpu) (1.26.11)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow-gpu) (2022.9.24)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow-gpu) (3.4)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow-gpu) (2.1.1)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.7/site-packages (from werkzeug>=1.0.1->tensorboard<2.11,>=2.10->tensorflow-gpu) (2.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.11,>=2.10->tensorflow-gpu) (3.10.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow-gpu) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow-gpu) (3.2.2)\n",
      "Installing collected packages: libclang, keras, flatbuffers, tensorflow-io-gcs-filesystem, tensorflow-estimator, protobuf, numpy, absl-py, tensorboard, tensorflow-gpu\n",
      "  Attempting uninstall: keras\n",
      "    Found existing installation: keras 2.6.0\n",
      "    Uninstalling keras-2.6.0:\n",
      "      Successfully uninstalled keras-2.6.0\n",
      "  Attempting uninstall: flatbuffers\n",
      "    Found existing installation: flatbuffers 1.12\n",
      "    Uninstalling flatbuffers-1.12:\n",
      "      Successfully uninstalled flatbuffers-1.12\n",
      "  Attempting uninstall: tensorflow-estimator\n",
      "    Found existing installation: tensorflow-estimator 2.6.0\n",
      "    Uninstalling tensorflow-estimator-2.6.0:\n",
      "      Successfully uninstalled tensorflow-estimator-2.6.0\n",
      "  Attempting uninstall: protobuf\n",
      "    Found existing installation: protobuf 3.20.3\n",
      "    Uninstalling protobuf-3.20.3:\n",
      "      Successfully uninstalled protobuf-3.20.3\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.19.5\n",
      "    Uninstalling numpy-1.19.5:\n",
      "      Successfully uninstalled numpy-1.19.5\n",
      "  Attempting uninstall: absl-py\n",
      "    Found existing installation: absl-py 0.15.0\n",
      "    Uninstalling absl-py-0.15.0:\n",
      "      Successfully uninstalled absl-py-0.15.0\n",
      "  Attempting uninstall: tensorboard\n",
      "    Found existing installation: tensorboard 2.6.0\n",
      "    Uninstalling tensorboard-2.6.0:\n",
      "      Successfully uninstalled tensorboard-2.6.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tfx-bsl 1.10.1 requires google-api-python-client<2,>=1.7.11, but you have google-api-python-client 2.65.0 which is incompatible.\n",
      "tfx-bsl 1.10.1 requires pyarrow<7,>=6, but you have pyarrow 9.0.0 which is incompatible.\n",
      "tfx-bsl 1.10.1 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,<3,>=1.15.5, but you have tensorflow 2.6.0 which is incompatible.\n",
      "tensorflow 2.6.0 requires absl-py~=0.10, but you have absl-py 1.3.0 which is incompatible.\n",
      "tensorflow 2.6.0 requires flatbuffers~=1.12.0, but you have flatbuffers 22.10.26 which is incompatible.\n",
      "tensorflow 2.6.0 requires numpy~=1.19.2, but you have numpy 1.21.6 which is incompatible.\n",
      "tensorflow-transform 1.10.1 requires pyarrow<7,>=6, but you have pyarrow 9.0.0 which is incompatible.\n",
      "tensorflow-transform 1.10.1 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,<2.10,>=1.15.5, but you have tensorflow 2.6.0 which is incompatible.\n",
      "tensorflow-serving-api 2.10.0 requires tensorflow<3,>=2.10.0, but you have tensorflow 2.6.0 which is incompatible.\n",
      "tensorflow-io 0.21.0 requires tensorflow-io-gcs-filesystem==0.21.0, but you have tensorflow-io-gcs-filesystem 0.27.0 which is incompatible.\n",
      "apache-beam 2.42.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.6 which is incompatible.\n",
      "apache-beam 2.42.0 requires pyarrow<8.0.0,>=0.15.1, but you have pyarrow 9.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed absl-py-1.3.0 flatbuffers-22.10.26 keras-2.10.0 libclang-14.0.6 numpy-1.21.6 protobuf-3.19.6 tensorboard-2.10.1 tensorflow-estimator-2.10.0 tensorflow-gpu-2.10.0 tensorflow-io-gcs-filesystem-0.27.0\n",
      "Collecting tensorflow-io==0.25.0\n",
      "  Downloading tensorflow_io-0.25.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (23.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.4/23.4 MB\u001b[0m \u001b[31m53.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hCollecting tensorflow-io-gcs-filesystem==0.25.0\n",
      "  Downloading tensorflow_io_gcs_filesystem-0.25.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (2.1 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m85.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: tensorflow-io-gcs-filesystem, tensorflow-io\n",
      "  Attempting uninstall: tensorflow-io-gcs-filesystem\n",
      "    Found existing installation: tensorflow-io-gcs-filesystem 0.27.0\n",
      "    Uninstalling tensorflow-io-gcs-filesystem-0.27.0:\n",
      "      Successfully uninstalled tensorflow-io-gcs-filesystem-0.27.0\n",
      "  Attempting uninstall: tensorflow-io\n",
      "    Found existing installation: tensorflow-io 0.21.0\n",
      "    Uninstalling tensorflow-io-0.21.0:\n",
      "      Successfully uninstalled tensorflow-io-0.21.0\n",
      "Successfully installed tensorflow-io-0.25.0 tensorflow-io-gcs-filesystem-0.25.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow-io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Restart the kernel by clicking **Kernel > Restart Kernel > Restart**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by importing the necessary libraries for this lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-14 11:06:33.893200: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-09-14 11:06:34.076852: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2022-09-14 11:06:34.848088: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64\n",
      "2022-09-14 11:06:34.848205: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64\n",
      "2022-09-14 11:06:34.848219: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from tensorflow.keras.layers import (\n",
    "    CategoryEncoding,\n",
    "    Concatenate,\n",
    "    Dense,\n",
    "    Discretization,\n",
    "    Embedding,\n",
    "    Flatten,\n",
    "    Input,\n",
    ")\n",
    "from tensorflow.keras.layers.experimental.preprocessing import HashedCrossing\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load raw data \n",
    "\n",
    "We will use the taxifare dataset, using the CSV files that we created in the first notebook of this sequence. Those files have been saved into `../data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 jupyter jupyter 123590 Sep 14 11:04 ../data/taxi-test.csv\n",
      "-rw-r--r-- 1 jupyter jupyter 579055 Sep 14 11:04 ../data/taxi-train.csv\n",
      "-rw-r--r-- 1 jupyter jupyter 123114 Sep 14 11:04 ../data/taxi-valid.csv\n"
     ]
    }
   ],
   "source": [
    "!ls -l ../data/*.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use tf.data to read the CSV files\n",
    "\n",
    "We wrote these functions for reading data from the csv files above in the [previous notebook](2_dataset_api.ipynb). For this lab we will also include some additional engineered features in our model. In particular, we will compute the difference in latitude and longitude, as well as the Euclidean distance between the pick-up and drop-off locations. We can accomplish this by adding these new features to the features dictionary with the function `add_engineered_features` below. \n",
    "\n",
    "Note that we include a call to this function when collecting our features dict and labels in the `features_and_labels` function below as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_COLUMNS = [\n",
    "    \"fare_amount\",\n",
    "    \"pickup_datetime\",\n",
    "    \"pickup_longitude\",\n",
    "    \"pickup_latitude\",\n",
    "    \"dropoff_longitude\",\n",
    "    \"dropoff_latitude\",\n",
    "    \"passenger_count\",\n",
    "    \"key\",\n",
    "]\n",
    "LABEL_COLUMN = \"fare_amount\"\n",
    "DEFAULTS = [[0.0], [\"na\"], [0.0], [0.0], [0.0], [0.0], [0.0], [\"na\"]]\n",
    "UNWANTED_COLS = [\"pickup_datetime\", \"key\"]\n",
    "\n",
    "\n",
    "def features_and_labels(row_data):\n",
    "    label = row_data.pop(LABEL_COLUMN)\n",
    "    features = row_data\n",
    "\n",
    "    for unwanted_col in UNWANTED_COLS:\n",
    "        features.pop(unwanted_col)\n",
    "\n",
    "    return features, label\n",
    "\n",
    "\n",
    "def create_dataset(pattern, batch_size=1, mode=\"eval\"):\n",
    "    dataset = tf.data.experimental.make_csv_dataset(\n",
    "        pattern, batch_size, CSV_COLUMNS, DEFAULTS\n",
    "    )\n",
    "\n",
    "    dataset = dataset.map(features_and_labels)\n",
    "\n",
    "    if mode == \"train\":\n",
    "        dataset = dataset.shuffle(buffer_size=1000).repeat()\n",
    "\n",
    "    # take advantage of multi-threading; 1=AUTOTUNE\n",
    "    dataset = dataset.prefetch(1)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a Wide and Deep model in Keras\n",
    "\n",
    "To build a wide-and-deep network, we connect the sparse (i.e. wide) features directly to the output node, but pass the dense (i.e. deep) features through a set of fully connected layers. Here’s that model architecture looks using the Functional API.\n",
    "\n",
    "First, we'll create our input columns using [tf.keras.layers.Input](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Input)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_COLS = [\n",
    "    \"pickup_longitude\",\n",
    "    \"pickup_latitude\",\n",
    "    \"dropoff_longitude\",\n",
    "    \"dropoff_latitude\",\n",
    "    \"passenger_count\",\n",
    "]\n",
    "\n",
    "inputs = #TODO: Your code goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can connect each layers one by one, and define Wide and Deep model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-14 11:06:57.353820: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64\n",
      "2022-09-14 11:06:57.353875: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-09-14 11:06:57.353929: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tensorflow-2-6-20220914-153856): /proc/driver/nvidia/version does not exist\n",
      "2022-09-14 11:06:57.354205: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "dnn_hidden_units = [32, 8]\n",
    "NBUCKETS = 16\n",
    "\n",
    "latbuckets = np.linspace(start=38.0, stop=42.0, num=NBUCKETS).tolist()\n",
    "lonbuckets = np.linspace(start=-76.0, stop=-72.0, num=NBUCKETS).tolist()\n",
    "\n",
    "# Bucketization with Discretization layer\n",
    "plon = Discretization(lonbuckets, name=\"plon_bkt\")(inputs[\"pickup_longitude\"])\n",
    "plat = Discretization(latbuckets, name=\"plat_bkt\")(inputs[\"pickup_latitude\"])\n",
    "dlon = Discretization(lonbuckets, name=\"dlon_bkt\")(inputs[\"dropoff_longitude\"])\n",
    "dlat = Discretization(latbuckets, name=\"dlat_bkt\")(inputs[\"dropoff_latitude\"])\n",
    "\n",
    "# Feature Cross with HashedCrossing layer\n",
    "p_fc = HashedCrossing(num_bins=NBUCKETS * NBUCKETS, name=\"p_fc\")((plon, plat))\n",
    "d_fc = HashedCrossing(num_bins=NBUCKETS * NBUCKETS, name=\"d_fc\")((dlon, dlat))\n",
    "pd_fc = HashedCrossing(num_bins=NBUCKETS**4, name=\"pd_fc\")((p_fc, d_fc))\n",
    "\n",
    "# Embedding with Embedding layer\n",
    "pd_embed = Embedding(input_dim=NBUCKETS**4, output_dim=10, name=\"pd_embed\")(\n",
    "    pd_fc\n",
    ")\n",
    "\n",
    "# Concatenate and define inputs for deep network\n",
    "deep = Concatenate(name=\"deep_input\")(\n",
    "    [\n",
    "        inputs[\"pickup_longitude\"],\n",
    "        inputs[\"pickup_latitude\"],\n",
    "        inputs[\"dropoff_longitude\"],\n",
    "        inputs[\"dropoff_latitude\"],\n",
    "        Flatten(name=\"flatten_embedding\")(pd_embed),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Add hidden Dense layers\n",
    "for i, num_nodes in enumerate(dnn_hidden_units, start=1):\n",
    "    deep = Dense(num_nodes, activation=\"relu\", name=f\"hidden_{i}\")(deep)\n",
    "\n",
    "# Onehot Encoding with CategoryEncoding layer\n",
    "p_onehot = CategoryEncoding(num_tokens=NBUCKETS * NBUCKETS, name=\"p_onehot\")(\n",
    "    p_fc\n",
    ")\n",
    "d_onehot = CategoryEncoding(num_tokens=NBUCKETS * NBUCKETS, name=\"d_onehot\")(\n",
    "    d_fc\n",
    ")\n",
    "pd_onehot = CategoryEncoding(num_tokens=NBUCKETS**4, name=\"pd_onehot\")(pd_fc)\n",
    "\n",
    "# Concatenate and define inputs for wide network\n",
    "wide = Concatenate(name=\"wide_input\")([p_onehot, d_onehot, pd_onehot])\n",
    "\n",
    "# Concatenate wide & deep networks\n",
    "concat = Concatenate(name=\"concatenate\")([deep, wide])\n",
    "\n",
    "# Define the final output layer\n",
    "output = Dense(1, activation=None, name=\"output\")(concat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we'll define our custom RMSE evaluation metric and build our wide and deep model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(y_true, y_pred):\n",
    "    return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can define the input and output of the entire model, and compile it.  We can also use `plot_model` to see a diagram of the model we've created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=list(inputs.values()), outputs=output)\n",
    "model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[rmse])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(model, show_shapes=False, rankdir=\"LR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll set up our training variables, create our datasets for training and validation, and train our model.\n",
    "\n",
    "(We refer you the the blog post [ML Design Pattern #3: Virtual Epochs](https://medium.com/google-cloud/ml-design-pattern-3-virtual-epochs-f842296de730) for further details on why express the training in terms of `NUM_TRAIN_EXAMPLES` and `NUM_EVALS` and why, in this training code, the number of epochs is really equal to the number of evaluations we perform.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 100\n",
    "NUM_TRAIN_EXAMPLES = 10000 * 5  # training dataset will repeat, wrap around\n",
    "NUM_EVALS = 50  # how many times to evaluate\n",
    "NUM_EVAL_EXAMPLES = 10000  # enough to get a reasonable sample\n",
    "\n",
    "trainds = create_dataset(\n",
    "    pattern=\"../data/taxi-train*\", batch_size=BATCH_SIZE, mode=\"train\"\n",
    ")\n",
    "\n",
    "evalds = create_dataset(\n",
    "    pattern=\"../data/taxi-valid*\", batch_size=BATCH_SIZE, mode=\"eval\"\n",
    ").take(NUM_EVAL_EXAMPLES // 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      " 2/10 [=====>........................] - ETA: 0s - loss: 219.0831 - rmse: 14.6388 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-14 11:07:25.584603: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 26214400 exceeds 10% of free system memory.\n",
      "2022-09-14 11:07:25.597125: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 26419200 exceeds 10% of free system memory.\n",
      "2022-09-14 11:07:25.624584: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 26422400 exceeds 10% of free system memory.\n",
      "2022-09-14 11:07:25.634538: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 26422400 exceeds 10% of free system memory.\n",
      "2022-09-14 11:07:25.678516: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 26214400 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 3s 116ms/step - loss: 202.1554 - rmse: 14.0758 - val_loss: 238.0979 - val_rmse: 15.2160\n",
      "Epoch 2/50\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 170.7054 - rmse: 12.9997 - val_loss: 214.7816 - val_rmse: 14.4954\n",
      "Epoch 3/50\n",
      "10/10 [==============================] - 0s 42ms/step - loss: 170.2820 - rmse: 12.8802 - val_loss: 193.9719 - val_rmse: 13.6492\n",
      "Epoch 4/50\n",
      "10/10 [==============================] - 0s 41ms/step - loss: 157.0143 - rmse: 12.3660 - val_loss: 161.1315 - val_rmse: 12.5028\n",
      "Epoch 5/50\n",
      "10/10 [==============================] - 0s 40ms/step - loss: 130.7907 - rmse: 11.1099 - val_loss: 118.1145 - val_rmse: 10.6360\n",
      "Epoch 6/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 113.9507 - rmse: 10.5863 - val_loss: 109.0727 - val_rmse: 9.8226\n",
      "Epoch 7/50\n",
      "10/10 [==============================] - 0s 45ms/step - loss: 76.3530 - rmse: 8.6332 - val_loss: 114.4904 - val_rmse: 10.4660\n",
      "Epoch 8/50\n",
      "10/10 [==============================] - 0s 41ms/step - loss: 110.4223 - rmse: 10.2686 - val_loss: 96.3673 - val_rmse: 9.6532\n",
      "Epoch 9/50\n",
      "10/10 [==============================] - 0s 41ms/step - loss: 87.8784 - rmse: 9.2117 - val_loss: 126.1179 - val_rmse: 10.9460\n",
      "Epoch 10/50\n",
      "10/10 [==============================] - 0s 43ms/step - loss: 87.0494 - rmse: 9.1182 - val_loss: 111.4695 - val_rmse: 10.4287\n",
      "Epoch 11/50\n",
      "10/10 [==============================] - 0s 46ms/step - loss: 97.6954 - rmse: 9.7746 - val_loss: 106.1018 - val_rmse: 10.0064\n",
      "Epoch 12/50\n",
      "10/10 [==============================] - 0s 41ms/step - loss: 94.9626 - rmse: 9.5273 - val_loss: 118.4116 - val_rmse: 10.5981\n",
      "Epoch 13/50\n",
      "10/10 [==============================] - 0s 38ms/step - loss: 101.5457 - rmse: 9.9573 - val_loss: 113.1188 - val_rmse: 10.2043\n",
      "Epoch 14/50\n",
      "10/10 [==============================] - 0s 41ms/step - loss: 98.5455 - rmse: 9.7913 - val_loss: 101.8952 - val_rmse: 9.8497\n",
      "Epoch 15/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 95.8068 - rmse: 9.6597 - val_loss: 117.2711 - val_rmse: 10.6890\n",
      "Epoch 16/50\n",
      "10/10 [==============================] - 0s 43ms/step - loss: 85.0687 - rmse: 9.0759 - val_loss: 114.1567 - val_rmse: 10.2495\n",
      "Epoch 17/50\n",
      "10/10 [==============================] - 0s 38ms/step - loss: 85.6312 - rmse: 9.0984 - val_loss: 120.2505 - val_rmse: 10.7439\n",
      "Epoch 18/50\n",
      "10/10 [==============================] - 0s 41ms/step - loss: 92.2933 - rmse: 9.3993 - val_loss: 104.1734 - val_rmse: 10.0080\n",
      "Epoch 19/50\n",
      "10/10 [==============================] - 0s 44ms/step - loss: 77.3782 - rmse: 8.6853 - val_loss: 105.8701 - val_rmse: 10.0834\n",
      "Epoch 20/50\n",
      "10/10 [==============================] - 0s 37ms/step - loss: 82.0590 - rmse: 8.8888 - val_loss: 106.9739 - val_rmse: 10.2120\n",
      "Epoch 21/50\n",
      "10/10 [==============================] - 0s 43ms/step - loss: 126.4662 - rmse: 10.7840 - val_loss: 115.9705 - val_rmse: 10.3918\n",
      "Epoch 22/50\n",
      "10/10 [==============================] - 0s 40ms/step - loss: 125.4624 - rmse: 10.6178 - val_loss: 105.3069 - val_rmse: 10.0698\n",
      "Epoch 23/50\n",
      "10/10 [==============================] - 0s 41ms/step - loss: 84.6222 - rmse: 9.0904 - val_loss: 93.2469 - val_rmse: 9.3699\n",
      "Epoch 24/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 69.5479 - rmse: 8.2484 - val_loss: 101.2454 - val_rmse: 9.8844\n",
      "Epoch 25/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 101.9881 - rmse: 9.8599 - val_loss: 87.7264 - val_rmse: 9.2702\n",
      "Epoch 26/50\n",
      "10/10 [==============================] - 0s 41ms/step - loss: 94.1218 - rmse: 9.6389 - val_loss: 108.5248 - val_rmse: 10.0902\n",
      "Epoch 27/50\n",
      "10/10 [==============================] - 0s 43ms/step - loss: 83.1674 - rmse: 9.0135 - val_loss: 103.9562 - val_rmse: 10.0650\n",
      "Epoch 28/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 116.8981 - rmse: 10.4143 - val_loss: 103.2521 - val_rmse: 9.8328\n",
      "Epoch 29/50\n",
      "10/10 [==============================] - 0s 37ms/step - loss: 78.7830 - rmse: 8.8144 - val_loss: 117.0872 - val_rmse: 10.4916\n",
      "Epoch 30/50\n",
      "10/10 [==============================] - 0s 37ms/step - loss: 88.7629 - rmse: 9.2749 - val_loss: 94.4283 - val_rmse: 9.4113\n",
      "Epoch 31/50\n",
      "10/10 [==============================] - 0s 40ms/step - loss: 88.8424 - rmse: 9.2416 - val_loss: 114.7212 - val_rmse: 10.4367\n",
      "Epoch 32/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 81.6051 - rmse: 8.9380 - val_loss: 113.3341 - val_rmse: 10.4879\n",
      "Epoch 33/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 106.9547 - rmse: 10.0990 - val_loss: 96.2974 - val_rmse: 9.5825\n",
      "Epoch 34/50\n",
      "10/10 [==============================] - 0s 40ms/step - loss: 73.7097 - rmse: 8.4454 - val_loss: 115.4738 - val_rmse: 10.5110\n",
      "Epoch 35/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 89.9968 - rmse: 9.0317 - val_loss: 118.4246 - val_rmse: 10.7395\n",
      "Epoch 36/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 78.2209 - rmse: 8.7758 - val_loss: 115.8041 - val_rmse: 10.2237\n",
      "Epoch 37/50\n",
      "10/10 [==============================] - 0s 40ms/step - loss: 88.7727 - rmse: 9.0184 - val_loss: 96.8756 - val_rmse: 9.4473\n",
      "Epoch 38/50\n",
      "10/10 [==============================] - 0s 40ms/step - loss: 113.2748 - rmse: 10.2246 - val_loss: 92.1207 - val_rmse: 9.4717\n",
      "Epoch 39/50\n",
      "10/10 [==============================] - 0s 38ms/step - loss: 102.1175 - rmse: 9.9602 - val_loss: 99.1133 - val_rmse: 9.6095\n",
      "Epoch 40/50\n",
      "10/10 [==============================] - 0s 37ms/step - loss: 95.6664 - rmse: 9.4891 - val_loss: 96.3910 - val_rmse: 9.5516\n",
      "Epoch 41/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 104.7554 - rmse: 9.6263 - val_loss: 99.9310 - val_rmse: 9.8375\n",
      "Epoch 42/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 87.6835 - rmse: 9.2413 - val_loss: 106.5187 - val_rmse: 10.0703\n",
      "Epoch 43/50\n",
      "10/10 [==============================] - 0s 38ms/step - loss: 92.6625 - rmse: 9.3526 - val_loss: 102.2319 - val_rmse: 9.8566\n",
      "Epoch 44/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 95.3021 - rmse: 9.4960 - val_loss: 96.9003 - val_rmse: 9.5960\n",
      "Epoch 45/50\n",
      "10/10 [==============================] - 0s 38ms/step - loss: 78.9735 - rmse: 8.7833 - val_loss: 108.2826 - val_rmse: 10.1047\n",
      "Epoch 46/50\n",
      "10/10 [==============================] - 0s 38ms/step - loss: 72.5486 - rmse: 8.4293 - val_loss: 106.1392 - val_rmse: 10.0975\n",
      "Epoch 47/50\n",
      "10/10 [==============================] - 0s 38ms/step - loss: 71.8754 - rmse: 8.3644 - val_loss: 98.4696 - val_rmse: 9.6996\n",
      "Epoch 48/50\n",
      "10/10 [==============================] - 0s 39ms/step - loss: 80.1321 - rmse: 8.8913 - val_loss: 104.1982 - val_rmse: 10.0431\n",
      "Epoch 49/50\n",
      "10/10 [==============================] - 0s 40ms/step - loss: 79.0475 - rmse: 8.8130 - val_loss: 97.7188 - val_rmse: 9.6696\n",
      "Epoch 50/50\n",
      "10/10 [==============================] - 0s 42ms/step - loss: 73.2252 - rmse: 8.4129 - val_loss: 89.0993 - val_rmse: 9.1542\n",
      "CPU times: user 43.6 s, sys: 6.74 s, total: 50.3 s\n",
      "Wall time: 41.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "steps_per_epoch = NUM_TRAIN_EXAMPLES // (BATCH_SIZE * NUM_EVALS)\n",
    "\n",
    "OUTDIR = \"./taxi_trained\"\n",
    "shutil.rmtree(path=OUTDIR, ignore_errors=True)  # start fresh each time\n",
    "\n",
    "history = model.fit(# TODO: Your code goes here\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as before, we can examine the history to see how the RMSE changes through training on the train set and validation set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "RMSE_COLS = [\"rmse\", \"val_rmse\"]\n",
    "\n",
    "pd.DataFrame(history.history)[RMSE_COLS].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2022 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
   ]
  }
 ],
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  "kernelspec": {
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